How We Uncovered $3.5 Million Worth of Deals

January 3rd, 2018

SVP Sales Perspective

After 3 years in the role, this SVP of Sales had the sales team performing in terms of increased bookings and billings. Salesforce.com, plus a number of other cloud based sales tools, provided automation, efficiencies and visibility of the end to end pipeline. He wanted more… more insight that would increase deal velocity, renewals and rep all-performance.

The Nagging Question

He felt that if he looked at the data hard and long enough, those insights were in there. On opportunities, reps and customers.

Hurdles

Their data is in the cloud in different applications and in spreadsheets and internal systems such as customer support tickets.

No IT resources to clean and prepare the data for analysis.

No data scientists internally to build the models.

Data Preparation

Take database dumps of systems, export the cloud data and drop that and the spreadsheets onto an FTP server. 12 years worth of data in 200+ files totaling 5000 columns. Files were cleaned, prepared and pre-analyzed – in minutes. Charts were auto generated for business analyst to select the most useful with high correlations and hubs of data in the model. Analysts reviewed the significant files, millions of rows, but simply examining meta-data, automatically generated visualizations and finally settling upon about 20 variables that were put into a de-normalized table. Then the predictive models were brought into play. No configuration, no code.

Analysis

Using 20 existing variables that allowed a ClosedWin to be accurately predicted at the start of the quarter – when even the sales person is not willing to commit. Where the rep and the system agree that’s not an issue, but if the rep produces a low forecast and the algorithm says this deal will close, that is where the benefit opportunity existed. In a few days uncovering $3.5M of deals in the pipeline that should get a little more focus. Also looked across the systems to find the best customers not just on revenue, but customer servicing costs, returns, renewals and a number of other things

Insights

There were around 400 deals identified where the reps had given a low probability of closing where the analytical model had a high probability based on the predictive factors. This gap represented around $3.5M or 10% of the pipeline value.

Action

The reps were given the additional information regarding these deals and the deals are currently being monitored as they progress through the pipeline.